An increase in the efficiency of sampling from Boltzmann distributions would have a
significant impact on deep learning and other machine-learning applications. Recently …

We consider dense, associative neural-networks trained by a teacher (ie, with supervision)
and we investigate their computational capabilities analytically, via statistical-mechanics …

Density Peaks (DP) is a recently proposed clustering algorithm that has distinctive
advantages over existing clustering algorithms. It has already been used in a wide range of …

While Hopfield networks are known as paradigmatic models for memory storage and
retrieval, modern artificial intelligence systems mainly stand on the machine learning …

For performing regression tasks involved in various physics problems, enhancing the
precision or equivalently reducing the uncertainty of regression results is undoubtedly one of …

We consider dense, associative neural-networks trained with no supervision and we
investigate their computational capabilities analytically, via statistical-mechanics tools, and …

In this paper, we solve the inverse problem for the cubic mean-field Ising model. Starting
from configuration data generated according to the distribution of the model, we reconstruct …

Brain–computer interfaces have seen extraordinary surges in developments in recent years,
and a significant discrepancy now exists between the abundance of available data and the …

We propose an efficient algorithm to solve inverse problems in the presence of binary
clustered datasets. We consider the paradigmatic Hopfield model in a teacher student …

This paper presents a thorough examination of the thermodynamic limit of the pressure
function for the mean-field Ising model with four-body interaction. By utilizing a standard …